Forthcoming and Online First Articles

International Journal of Advanced Mechatronic Systems

International Journal of Advanced Mechatronic Systems (IJAMechS)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Advanced Mechatronic Systems (2 papers in press)

Regular Issues

  • Apple multi-index fusion classi?cation based on improved LSTM   Order a copy of this article
    by Shuhui Bi, Lisha Chen, Lei Wang, Xuehua Yan, Liyao Ma 
    Abstract: Apples non-destructive testing (NDT) based on near infrared (NIR) spectroscopy technology can predict the internal quality, but it mainly focuses on single internal index detection. In order to better reflect the internal quality of apple, multi-index fusion classification based on improved long and short-term memory (LSTM) neural network will be considered in this paper. Firstly, apples soluble solids content (SSC) and moisture content (MC) are selected as the key indexes, which are the main factors affecting the quality and taste of apple, and the correlation analysis of spectra with apples SSC and MC is carried out, and the correlation thermal maps are drawn. Secondly, multiple scattering correction (MSC) is used for pre-processing to correct the baseline translation and drift of spectral data. Moreover, sparse principal component analysis (SPCA) is combined with to reduce the spectral data dimension, enhancing the models predictive precision. Then, the improved particle swarm optimisation (IPSO) algorithm is employed to optimise the LSTM multi-index prediction model. Finally, the simulation results indicate that the SSC and MC classification accuracy by the proposed IPSO-LSTM model substantially surpasses that by LSTM model.
    Keywords: near infrared spectroscopy technology; multi-index; long and short-term memory neural network; apple classification.

Special Issue on: RAISE2023 Mechatronics, Automation and Cyber-Physical Manufacturing Systems

  • Modern usage in representation reconstruction methods: an empirical way of GAN to provide solutions for multiple sectors   Order a copy of this article
    by Rohit Rastogi, Vineet Rawat, Sidhant Kaushal 
    Abstract: Image restoration poses a formidable challenge in the field of computer vision, endeavouring to restore high-quality images from degraded or corrupted versions. This research paper conducts a comprehensive comparison of three prominent image restoration methodologies: GFP GAN, DeOldify, and MIRNet. GFP GAN, featuring a specialised GAN architecture designed for image restoration tasks, introduces an AI-centric approach. DeOldify, a deep learning-based method, focuses on colourising and restoring old images using advanced AI techniques, while MIRNet offers a lightweight network specifically crafted for image restoration within an AI framework. The comparative analysis involves training and testing each method on a diverse dataset comprising both degraded and ground truth images. Employing a confusion matrix, precision, accuracy, recall, and other evaluation metrics are computed to comprehensively assess the performance of these AI-based methods. The matrix affords insights into the strengths and weaknesses of each AI-driven approach, providing a nuanced understanding of their respective performances.
    Keywords: GFP GAN; DeOldify; MIRNet; confusion matrix; restoration; image enhancement; computer vision; AI techniques; precision; accuracy; recall; noise; blur.

 

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